Satellite Remote Sensing for Estimating Reservoir Physical Characteristics: A Global Review of Existing Methodologies for Operational Monitoring

Codjia, Audrey Kantz Dossou; Akpoti, Komlavi; Dembélé, Moctar; Yonaba, Roland; Fowe, Tazen; Orowale, Triumph Prosper; Koissi, Modeste G. Déo-Gratias; Zwart, Sander J.

Satellite Remote Sensing for Estimating Reservoir Physical Characteristics: A Global Review of Existing Methodologies for Operational Monitoring

Autores

Codjia, Audrey Kantz Dossou

Orowale, Triumph Prosper

Koissi, Modeste G. Déo-Gratias

Zwart, Sander J.

Editor

Elsevier

Descripción

Accurate and timely estimates of reservoir surface area, water level, and volume are essential for water resource management. Yet no recent synthesis compares the remote sensing methods used to obtain these physical characteristics. This study evaluates peer-reviewed studies from 2000 to 2025 that derived any of the three characteristics from satellite data to identify reliable techniques and operational gaps. A total of 169 cases of surface area mapping (88), water level retrieval (49), and volume estimation (32) were analyzed from 106 articles across more than 60 countries. Each case was classified according to its physical characteristics, approach, sensor, and validation method. Surface area is typically mapped using optical imagery (76 %). Threshold indices dominate at 63 %. Meanwhile, machine and deep learning methods are being used more frequently to provide more accurate classifications. Water levels are usually obtained from radar altimetry (67 %) followed by areaelevation models (30 %). Volume is most often computed using combined area-elevation approaches (60 %), followed by water level-volume regressions (25 %) and area-volume curves (15 %), with average errors of up to 10 %. Three critical gaps emerge: only 11 % of studies address reservoirs smaller than 1 km2 , turbid or vegetated waters incur estimation errors, and only a few studies use sensors with a revisit time of three days or less, which limits real-time management. Although fusion of several sensor data is demonstrably more accurate, it remains rare. These insights guide managers and future research directions to enable automated, high-resolution monitoring of both large and small reservoirs.